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Tissue Specificity Based Isoform Function Prediction

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 3048-3059页

作者： Yu, Guoxian Huang, Qiuyue Zhang, Xiangliang Guo, Maozu Wang, Jun Shandong Univ Sch Software Jinan 250101 Shandong Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res Jinan 250101 Shandong Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 23955 Saudi Arabia Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China

Alternative splicing enables a gene spliced into different isoforms and hence protein variants. Identifying individual functions of these isoforms help deciphering the functional diversity of proteins. Although much efforts have been made for automatic gene function prediction, few efforts have been moved toward computational isoform function prediction, mainly due to the unavailable (or scanty) functional annotations of isoforms. Existing efforts directly combine multiple RNA-seq datasets without account of the important tissue specificity of alternative splicing. To bridge this gap, we introduce a novel approach called TS-Isofun to predict the functions of isoforms by integrating multiple functional association networks with respect to tissue specificity. TS-Isofun first constructs tissue-specific isoform functional association networks using multiple RNA-seq datasets from tissue-wise. Next, TS-Isofun assigns weights to these networks and models the tissue specificity by selectively integrating them with adaptive weights. It then introduces a joint matrix factorization-based data fusion model to leverage the integrated network, gene-level data and functional annotations of genes to infer the functions of isoforms. To achieve coherent weight assignment and isoform function prediction, TS-Isofun jointly optimizes the weights of individual networks and the isoform function prediction in a unified objective function. Experimental results show that TS-Isofun significantly outperforms state-of-the-art methods and the account of tissue specificity contributes to more accurate isoform function prediction.

关键词： Annotations Proteins Splicing RNA Optimization Support vector machines Heterogeneous networks Isoform function prediction tissue specificity Joint matrix factorization data fusion alternative splicing

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MSCAP: DNA Methylation Age Predictor based on Multiscale Convolutional Neural Network

MSCAP: DNA Methylation Age Predictor based on Multiscale Con...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Han Cai, Ruirui Zong, Xizeng He, Zhiquan Zhang, Li Northeast Normal University School of Information Science and Technology Institute of Computational Biology Changchun China South China University of Technology Clinical Research Centre Guangzhou First People's Hospital School of Medicine Guangzhou China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Changchun University of Technology School of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

DNA methylation can reflect age-related issues in individuals, and age prediction tools developed based on DNA methylation are called Epigenetic clocks. Epigenetic clocks also serve as potent tools for enhancing researchers' understanding of the aging process and aim to contribute to the advancement of aging research. The current prevailing approach involves creating clocks using Elastic Net regression modeling, wherein high-age-correlation CpG sites were selected as features in advance. However, pre-selecting CpG sites in advance may inadvertently overlook interactions between distant CpG sites. Deep learning has significant advantages in dealing with vast feature sets due to the tremendous opportunities that the development of deep learning presents for research. Deep learning architectures have demonstrated greater efficiency in acquiring and representing large numbers of intricate features compared to traditional machine learning models. In this study, we proposed a deep learning model based on a multiscale convolutional neural network (MSCNN) for pan-tissue age prediction, called MSCAP. First, for the model to efficiently predict datasets from different sequencing platforms, we chose a common intersection of sites from the Illumina 27K and Illumina 450K platforms (25,789 CpG sites), then trained and tested the model on 93 datasets. Different tissues of the human body are used in the datasets for training and testing. And independently tested on 7 independent datasets against an Epigenetic clock developed by Horvath in 2013 with high prediction accuracy. The results showed that MSCAP could effectively extract key features from a vast number of potential features without advanced feature selection. In addition, MSCAP outperformed the Horvath clock on all seven independent test datasets. This research helped boost the development of epigenetic clock methods and reinforced the value of deep learning in computational biology. © 2023 ieee.

关键词： age prediction DNA methylation epigenetic clock multiscale convolutional neural network (MSCNN)

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DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第4期19卷 2166-2176页

作者： Yu, Guoxian Yang, Yeqian Yan, Yangyang Guo, Maozu Zhang, Xiangliang Wang, Jun Shandong Univ Sch Software Jinan 250101 Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res Jinan 250101 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China King Abdullah Univ Sci & Technol Thuwal 23955 Saudi Arabia

Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the associations between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses gene-isoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two sub-networks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC (area under the precision-recall curve) of 0.9141, macro F-measure of 0.9155, G-mean of 0.9278 and balanced accuracy of 0.9303 across 732 diseases, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborates the superiority of DeepIDA. The code of DeepIDA is available at http://***/***?name=DeepIDA.

关键词： Diseases Splicing Annotations Proteins Pathology Feature extraction Data models Isoform-disease associations alternative splicing deep neural networks data fusion class imbalance

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OmniReprodubileCellAnalysis: a comprehensive toolbox for the analysis of cellular biology data

OmniReprodubileCellAnalysis: a comprehensive toolbox for the...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Tortarolo, Dora Pernice, Simone Clapero, Fabiana Valdembri, Donatella Serini, Guido Riccardo, Federica Tarone, Lidia Bena, Chiara Enrico Bosia, Carla Contaldo, Sandro Gepiro Beccuti, Marco Pennisi, Marzio Cordero, Francesca University of Turin Department of Computer Science Turin Italy Department of Oncology Turin Italy University of Turin Department of Molecular Biotechnology and Health Sciences Turin Italy Italian Institute for Genomic Medicine Turin Italy Université Paris-Saclay Inrae AgroParisTech Micalis Institute Jouy-Rn-Josas78350 France Polytechnic University of Turin Department of Applied Science and Technology Turin Italy University of Eastern Piedmont Computer Science Institute DiSIT Alessandria Italy

ISBN: (纸本)9798350337488

Open science and reproducibility are two key pillars of modern scientific research. Open science is making scientific research and data accessible and transparent to the broader scientific community and the public. Reproducibility, on the other hand, is the ability to replicate and confirm research results by following the same methods and procedures. Reproducibility is thus crucial because it ensures the reliability and validity of scientific findings. The relationship between open science and reproducibility is intertwined;indeed open science practices, such as sharing raw data, detailed methodologies, and code, greatly facilitate the reproducibility of research. In recent years, concerns about the reproducibility of scientific research have gained prominence, and indeed scientists still lament the lack of details in the methods sections of published papers and the unavailability of raw data from the *** assist cellular biologists and immunologists and to promote a more transparent, open and reproducible research practice, we developed OmniReproducibleCellAnalysis (ORCA), a new Shiny Application based in R, for the semi-automated analysis of Western Blot (WB), Reverse Transcription-quantitative PCR (RT-qPCR), Enzyme-Linked ImmunoSorbent Assay (ELISA), Endocytosis and Cytotoxicity experiments. ORCA is open-source and approachable by scientists without advanced R language knowledge. Our application automatically compiles a report containing the finalized data analysis and all its preliminary and intermediate steps, ensuring data analysis standardization and reproducibility. Furthermore, ORCA allows to upload raw data and results directly on the data repository Harvard Dataverse, a valuable tool for promoting transparency and data accessibility in scientific *** employing ORCA, scientists will cut down analysis time and human-dependent errors, while taking a step towards a research practice compliant with Open Science and FAIR principle. © 2023 ieee.

关键词： Cellular biology computational Immunology computational Model Data Analysis Data Integration Immunology

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Cross-Attention Based Multi-Resolution Feature Fusion Model for Self-Supervised Cervical OCT Image Classification

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第4期20卷 2541-2554页

作者： Wang, Qingbin Chen, Kaiyi Dou, Wanrong Ma, Yutao Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Cent China Normal Univ Sch Comp Sci Wuhan 430079 Peoples R China Cent China Normal Univ Hubei Prov Key Lab Artificial Intelligence & Smart Wuhan 430079 Peoples R China

Cervical cancer seriously endangers the health of the female reproductive system and even risks women's life in severe cases. Optical coherence tomography (OCT) is a non-invasive, real-time, high-resolution imaging technology for cervical tissues. However, since the interpretation of cervical OCT images is a knowledge-intensive, time-consuming task, it is tough to acquire a large number of high-quality labeled images quickly, which is a big challenge for supervised learning. In this study, we introduce the vision Transformer (ViT) architecture, which has recently achieved impressive results in natural image analysis, into the classification task of cervical OCT images. Our work aims to develop a computer-aided diagnosis (CADx) approach based on a self-supervised ViT-based model to classify cervical OCT images effectively. We leverage masked autoencoders (MAE) to perform self-supervised pre-training on cervical OCT images, so the proposed classification model has a better transfer learning ability. In the fine-tuning process, the ViT-based classification model extracts multi-scale features from OCT images of different resolutions and fuses them with the cross-attention module. The ten-fold cross-validation results on an OCT image dataset from a multi-center clinical study of 733 patients in China indicate that our model achieved an AUC value of 0.9963 +/- 0.0069 with a 95.89 +/- 3.30% sensitivity and 98.23 +/- 1.36 % specificity, outperforming some state-of-the-art classification models based on Transformers and convolutional neural networks (CNNs) in the binary classification task of detecting high-risk cervical diseases, including high-grade squamous intraepithelial lesion (HSIL) and cervical cancer. Furthermore, ourmodel with the cross-shaped voting strategy achieved a sensitivity of 92.06% and specificity of 95.56% on an external validation dataset containing 288 three-dimensional (3D) OCT volumes from118 Chinese patients in a different new hospital. This result

关键词： Cervical cancer cross-attention optical coherence tomography self-supervised learning vision transformer

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18th International conference on Practical Applications of computational biology and bioinformatics, PACBB 2024

18th International Conference on Practical Applications of C...

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18th International conference on Practical Applications of computational biology and bioinformatics, PACBB 2024

ISBN: (纸本)9783031878725

The proceedings contain 21 papers. The special focus in this conference is on Practical Applications of computational biology and bioinformatics. The topics include: New Medical Image Hybrid Watermarking;PPI Prediction from Sequences via Transfer Learning on Balanced but yet Biased Datasets: An Open Problem;transformer-Enhanced Pathogenicity Prediction with Soft Labels in a Semi-supervised Setup;colon Cancer Survival Prediction from Epithelium Nuclei Morphological Features Within Histology Slides Using Deep Learning;Deep Learning-Based Approach for Early Detection of Moroccan Women Breast Cancer: A BIRADS Scoring System Perspective;Validating RetinaNet for the Object Detection-Based Mitosis Detection in the MIDOG Challenge;Protocol for a Graph Database of NTDs for Drug Repurposing;fcodes: A Straightforward Algorithm for Kinships Encoding;survival Analysis in Hematopoietic Stem Cell Transplantation for Multiple Myeloma: Methodology and Survival Predictions;probabilistic Algorithm Based on Cellular Automata for Parallel Tumor Growth Simulations;artificial intelligence Models for the Automating Papanicolaou Test Reading: A Systematic Review Toward Understanding Clinical Application;Application of YOLOv8 and a Model Based on Vision Transformers and UNet for LVNC Diagnosis: Advantages and Limitations;PDO Wine Grape Must Variety Classification and Model Interpretation;automated Machine Learning to Predict the Precursors of Plant Specialized Metabolites;using Machine Learning for Depression Detection Based on Gut Microbiome;exploring Methane Mitigation Strategies in Photosynthetic Microorganisms Through Genome-Scale Metabolic Models;iplants: A Repository of Plant Metabolic Data;revolutionizing Next-Generation Sequencing: Advanced Analysis Pipeline and Integrated Solutions;CNN Models in Prediction of Feno Type from Genotype.

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Identification of Hub Genes and Key Pathways in TNF-α and IFN-γ Induced Cytokine Storms via bioinformatics 10

Identification of Hub Genes and Key Pathways in TNF-α and I...

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10th International conference on bioinformatics and computational biology, ICBCB 2022

作者： Mailem, Ryan Christian Tayo, Lemmuel L. Mapúa University School of Chemical Biological and Materials Engineering and Sciences Metro Manila Philippines

ISBN: (纸本)9781665401081

Cytokine storms, an overaggressive immune response due to the overexpression of pro-inflammatory cytokines, have been identified to play a significant role in COVID-19 infections. Studies have shown that TNF-α and IFN-γ are integral to the process, however, its genetic mechanisms have yet to be fully elucidated. Herein, the key changes in the gene expression of TNF-α and IFN-γ induced cytokine storms are identified through differential gene analysis on the publicly available GEO GSE160163 dataset. GO and KEGG enrichment were used to annotate identified DEGs, and a PPI network was constructed based on the STRING database. A total of 446 differentially expressed genes were identified. Up-regulated genes and downregulated genes were enriched in viral immune response and infection pathways, and steroid biosynthesis and metabolic pathways, respectively. PPI construction revealed 1,834 interactions between 428 proteins, indicating their biological connectivity. Module analysis identified nine (9) hub genes: STAT1, CXCL10, CD274, CXCL9, IRF1, PSMB9, CD86, STAT3, and CXCR4, involved in viral immune response and three (3) significant modules involved in NOD-like receptor signaling, steroid biosynthesis, and viral infections. These identified DEGs, hub genes, and their respective enriched pathways aid us in understanding the molecular mechanisms of cytokine storms, as well as provide potential gene targets and druggable receptors for the treatment of cytokine storms. © 2022 ieee.

关键词： Storms

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Welcome to ieee CIBCB 2022

2022 IEEE Conference on Computational Intelligence in Bioinf...

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2022 ieee conference on computational intelligence in bioinformatics and computational biology, CIBCB 2022 2022年

作者： Ashlock, Daniel Fogel, Gary Houghten, Sheridan Brown, Joseph Hughes, James Fogel, Gary Houghten, Sheridan Allmendinger, Richard Auephanwiriyakul, Sansanee Ruz, Gonzalo Stoodley, Matthew Dubé, Michael Saunders, Amanda Nobile, Marco Ruz, Gonzalo Li, Yifeng Tchagang, Alain

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COVID-19 Knowledge Mining Based on Large Language Models and Chain-of-Thought Reasoning

COVID-19 Knowledge Mining Based on Large Language Models and...

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2024 ieee International conference on bioinformatics and Biomedicine, BIBM 2024

作者： Zhang, Fushuai Ling, Yunchao Yang, Jiaxin Zhang, Peng Zhang, Guoqing University of Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study Hangzhou China University of Chinese Academy of Sciences Chinese Academy of Sciences Cas Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai Institute of Nutrition and Health Shanghai China

ISBN: (纸本)9798350386226

This research aims to extract knowledge related to COVID-19 clinical diagnosis and molecular mechanisms from COVID-19 literature by applying large language models, deepening our quantitative understanding of COVID-19 and its causative virus SARS-CoV-2, and providing foundational data resources for subsequent COVID-19 research. The knowledge extraction process is divided into three stages:The first phase: A total of 131 key documents, including COVID-19 research, vaccine clinical trials, and clinical guidelines, were selected for content extraction. Entities, relationships, entity types, and conditions were annotated, and knowledge was described in the form of triplets, using events as the unit. By summarizing the important and frequently occurring events, entities, and relationships, as well as analyzing the clinical diagnosis knowledge of COVID-19, nine COVID-19 clinical diagnosis knowledge patterns were identified. Subsequently, a corpus containing 342,287 articles was constructed using search strategies from PubMed and the CORD-19 *** Second phase: Chain-of-thought reasoning was applied to enhance the data, simulating human experts in determining the knowledge patterns of COVID-19 sentences and extracting the COVID-19 entities and their types through a step-by-step reasoning *** Third phase: The focus was on training large language models to achieve superior Knowledge Pattern Discrimination (KPD) and Named Entity Recognition (NER) performance, the Accuracy of the KPD model and the F1-score of the NER model reached 0.869 and 0.887 respectively, surpassing existing methods. Additionally, ablation studies indicated that the data formats used in chain-of-thought reasoning and model fine-tuning affect the performance of the models. © 2024 ieee.

关键词： COVID-19

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scBKAP: A Clustering Model for Single-Cell RNA-Seq Data Based on Bisecting K-Means

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2023年第3期20卷 2007-2015页

作者： Wang, Xiaolin Gao, Hongli Qi, Ren Zheng, Ruiqing Gao, Xin Yu, Bin Qingdao Univ Sci & Technol Coll Math & Phys Qingdao 266061 Shandong Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Hunan Peoples R China King Abdullah Univ Sci & Technol KAUST Computat Biosci Res Ctr CBRC Thuwal 23955 Saudi Arabia Qingdao Univ Sci & Technol Coll Informat Sci & Technol Sch Data Sci Qingdao 266061 Shandong Peoples R China Univ Sci & Technol China Sch Data Sci Hefei 230027 Anhui Peoples R China

Advances in single-cell RNA sequencing (scRNA-seq) technologies allow researchers to analyze the genome-wide transcription profile and to solve biological problems at the individual-cell resolution. However, existing clustering methods on scRNA-seq suffer from high dropout rate and curse of dimensionality in the data. Here, we propose a novel pipeline, scBKAP, the cornerstone of which is a single-cell bisecting K-means clustering method based on an autoencoder network and a dimensionality reduction model MPDR. Specially, scBKAP utilizes an autoencoder network to reconstruct gene expression values from scRNA-seq data to alleviate the dropout issue, and the MPDR model composed of the M3Drop feature selection algorithm and the PHATE dimensionality reduction algorithm to reduce the dimensions of reconstructed data. The dimensionality-reduced data are then fed into the bisecting K-means clustering algorithm to identify the clusters of cells. Comprehensive experiments demonstrate scBKAP's superior performance over nine state-of-the-art single-cell clustering methods on 21 public scRNA-seq datasets and simulated datasets. The source codes and datasets are available at https://***/YuBinLab-QUST/scBKAP/ and https://***/10.24433/CO.4592131.v1.

关键词： Clustering algorithms Data models Pipelines Dimensionality reduction Clustering methods Standards Sequential analysis Autoencoder network bisecting K-means clustering Index Terms MPDR dimensionality reduction scRNA-seq

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